Natural human tRNA<sup>Ala</sup>anticodon variants mistranslate the genetic code
Bibliographic record
Abstract
Transfer RNAs (tRNAs) play an essential role in protein synthesis by linking the nucleic acid sequences of gene products to the amino acid sequences of proteins. There are >400 functional tRNA genes in humans, and adding to this diversity, there are many single-nucleotide polymorphisms in tRNAs across our population, including anticodon variants that mistranslate the genetic code. In human genomes, we identified three rare alanine tRNA (tRNA Ala ) variants with nonsynonymous anticodon mutations: tRNA Ala CGC G35T, tRNA Ala UGC G35A, and tRNA Ala AGC C36T. Since alanyl-tRNA synthetase (AlaRS) does not recognize the anticodon, we hypothesized that these tRNA Ala variants will misincorporate Ala at glutamate (Glu), valine (Val), and threonine (Thr) codons, respectively. We found that expressing the naturally occurring tRNA Ala variants in human cells led to defects in protein production without a substantial impact on cell growth. Using mass spectrometry, we confirmed and estimated Ala misincorporation levels at Glu (0.7%), Val (5%), and Thr (0.1%) codons. Although Ala misincorporation was higher at Val codons, cells misincorporating Ala at Glu codons had the most severe defect in protein production. The data demonstrate the ability of natural human tRNA Ala variants to generate mistranslation, leading to defects in protein production that depend on the nature of the amino acid replacement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".